SOTAVerified

Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 941–950 of 1706 papers

TitleStatusHype
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning—0
End-to-End Optimized Speech Coding with Deep Neural Networks—0
Energy-based Models for Video Anomaly Detection—0
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach—0
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory—0
Enhancement of Feature Engineering for Conditional Random Field Learning in Chinese Word Segmentation Using Unlabeled Data—0
Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach—0
Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble—0
Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach—0
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified